R: Predictions from Bagging Trees

predict.classbagg {ipred} | R Documentation

## Predictions from Bagging Trees

### Description

Predict the outcome of a new observation based on multiple trees.

### Usage

    ## S3 method for class 'classbagg'
    predict(object, newdata=NULL, type=c("class", "prob"),
                                aggregation=c("majority", "average", "weighted"), ...)
    ## S3 method for class 'regbagg'
    predict(object, newdata=NULL, aggregation=c("average",
                    "weighted"), ...)
    ## S3 method for class 'survbagg'
    predict(object, newdata=NULL,...)

### Arguments

`object` | object of classes `classbagg`, `regbagg` or `survbagg`.
`newdata` | a data frame of new observations.
`type` | character string denoting the type of predicted value returned for classification trees. Either `class` (predicted classes are returned) or `prob` (estimated class probabilities are returned).
`aggregation` | character string specifying how to aggregate, see below.
`...` | additional arguments, currently not passed to any function.

### Details

There are (at least) three different ways to aggregate the predictions of bagging classification trees. Most famous is class majority voting (`aggregation="majority"`) where the most frequent class is returned. The second way is choosing the class with maximal averaged class probability (`aggregation="average"`). The third method is based on the "aggregated learning sample", introduced by Hothorn et al. (2003) for survival trees. The prediction of a new observation is the majority class, mean or Kaplan-Meier curve of all observations from the learning sample identified by the `nbagg` leaves containing the new observation. For regression trees, only averaged or weighted predictions are possible.

By default, the out-of-bag estimate is computed if `newdata` is NOT specified. Therefore, the predictions of `predict(object)` are "honest" in some way (this is not possible for combined models via `comb` in `bagging`). If you like to compute the predictions for the learning sample itself, use `newdata` to specify your data.

### Value

The predicted class or estimated class probabilities are returned for classification trees. The predicted endpoint is returned in regression problems and the predicted Kaplan-Meier curve is returned for survival trees.

### References

Leo Breiman (1996), Bagging Predictors. Machine Learning 24(2), 123–140.

Torsten Hothorn, Berthold Lausen, Axel Benner and Martin Radespiel-Troeger (2004), Bagging Survival Trees. Statistics in Medicine, 23(1), 77–91.

### Examples

    data("Ionosphere", package = "mlbench")
    Ionosphere$V2 <- NULL # constant within groups

    # nbagg = 10 for performance reasons here
    mod <- bagging(Class ~ ., data=Ionosphere)

    # out-of-bag estimate

    mean(predict(mod) != Ionosphere$Class)

    # predictions for the first 10 observations

    predict(mod, newdata=Ionosphere[1:10,])

    predict(mod, newdata=Ionosphere[1:10,], type="prob")

[Package ipred version 0.9-6 Index]